DESI 2024: Survey overview and first cosmological results

Arnaud de Mattia - CEA Saclay

Hendaye, August 23rd

- physics motivation

- the survey

- first results

- what's next?

DESI maps galaxies

  • photometric surveys provide angular positions of galaxies

R.A.

Dec.

DESI maps galaxies

  • photometric surveys provide angular positions of galaxies
  • spectroscopic measurements for the redshift (radial) dimension

R.A.

Dec.

R.A.

Dec.

z

DESI 3D Map

Physics program
- Galaxy and quasar clustering
- Lyman-alpha forest
- Clusters and cross-correlations
- Galaxy and quasar physics
- Milky Way Survey
- Transients and low-z

DESI 3D Map

Physics program
- Galaxy and quasar clustering
- Lyman-alpha forest
- Clusters and cross-correlations
- Galaxy and quasar physics
- Milky Way Survey
- Transients and low-z

Clustering measurements

2-pt correlation function \(\xi(s)\): excess probability of finding two galaxies seperated by a given separation \(s\)

 

Clustering measurements

2-pt correlation function \(\xi(s)\): excess probability of finding two galaxies seperated by a given separation \(s\)

Power spectrum \(P(k) = \mathrm{FT}(\xi(s))\)

Baryon acoustic oscillations

Sound waves in primordial plasma

At recombination (\(z \sim 1100\))

  • plasma changes to optically thin
  • baryons decouple from photons
  • sound wave stalls

Baryon acoustic oscillations

Sound waves in primordial plasma

spherical shell in the distribution of galaxies, of radius the distance that sound waves travelled

= sound horizon scale at the drag epoch \( r_\mathrm{d} \sim 150 \; \mathrm{Mpc} \sim 100 \; \mathrm{Mpc}/h \)

At recombination (\(z \sim 1100\))

  • plasma changes to optically thin
  • baryons decouple from photons
  • sound wave stalls

Baryon acoustic oscillations

Sound waves in primordial plasma

spherical shell in the distribution of galaxies, of radius the distance that sound waves travelled

= sound horizon scale at the drag epoch \( r_\mathrm{d} \sim 150 \; \mathrm{Mpc} \sim 100 \; \mathrm{Mpc}/h \)

At recombination (\(z \sim 1100\))

  • plasma changes to optically thin
  • baryons decouple from photons
  • sound wave stalls

standard ruler

  • transverse to the line-of-sight: \(D_\mathrm{M}(z) / r_\mathrm{d}\)
z
\theta_\mathrm{BAO} = r_\mathrm{d} / D_\mathrm{M}(z)

BAO measurements

transverse comoving distance

sound horizon \(r_d\)

  • transverse to the line-of-sight: \(D_\mathrm{M}(z) / r_\mathrm{d}\)
  • along the line-of-sight: \(D_\mathrm{H}(z) / r_\mathrm{d} = c / (H(z) r_\mathrm{d}) \)

BAO measurements

z
\Delta z_\mathrm{BAO} = r_\mathrm{d} / D_\mathrm{H}(z)

Hubble distance

sound horizon \(r_d\)

  • transverse to the line-of-sight: \(D_\mathrm{M}(z) / r_\mathrm{d}\)
  • along the line-of-sight: \(D_\mathrm{H}(z) / r_\mathrm{d} = c / (H(z) r_\mathrm{d}) \)

At multiple redshifts \(z\)

BAO measurements

z_1
z_2
z_3

Probes the expansion history, hence the energy content (DE)

Absolute size at \(z = 0\): \(H_0 r_d\)

Full shape analysis

observed redshift = Hubble flow

Full shape analysis

observed redshift = Hubble flow and peculiar velocities (RSD = "redshift space distortions")

\propto f\sigma_8

Full shape also driven by primordial physics (\(\omega_m, \omega_b, n_s, f_{\mathrm{NL}}, ...\))

RSD probes growth of structure \(f\sigma_8\), sensitive to gravity, DE, \(\nu\)

DESI: a stage IV survey

Schlegel et al. 2022, arXiv:2209.03585

DESI vs SDSS

Mirror diameter 2.5 m 4 m
Number of fibers 1000 5000
Troughput ~20% 20%-50%
Spectro resolution 1560 - 2650 2000 - 5000

x20 survey speed and x2 resolution!

DESI Y5 galaxy samples

Bright Galaxies: 14M (SDSS: 600k)

0 < z < 0.4

LRG: 8M (SDSS: 1M)

0.4 < z < 0.8

ELG: 16M (SDSS: 200k)

0.6 < z < 1.6

QSO: 3M (SDSS: 500k)

Lya \(1.8 < z\)

Tracers \(0.8 < z < 2.1\)

Y5 \(\sim 40\)M galaxy redshifts!

\(z = 0.4\)

\(z = 0.8\)

\(z = 0\)

\(z = 1.6\)

\(z = 2.0\)

\(z = 3.0\)

DESI Y5 forecasts

Survey Validation (DESI Collaboration, arXiv:2306.06307)

BAO and RSD constraints at the end of the survey (\( \Delta z = 0.1 \))

Ly\(\alpha\)

DESI Y5 forecasts

Survey Validation (DESI Collaboration, arXiv:2306.06307)

BAO and RSD constraints at the end of the survey (\( \Delta z = 0.1 \))

Ly\(\alpha\)

(w/ Planck)

Thanks to our sponsors and

72 Participating Institutions!

Thanks to our sponsors and

72 Participating Institutions!

900 researchers

DOE funds the DESI project:

- operations ($12M/year)

- construction ($56M)

DOE funds the DESI project:

- operations ($12M/year)

- construction ($56M)

+ other sources ($19M, inc. in kind)

= $75M

Lawrence Berkeley National Lab (LBNL) is the managing laboratory

LBNL hosts NERSC, the computing facility for data processing

Mayall telescope at Kitt Peak National Observatory near Tucson, Arizona

Part of the NSF’s National Optical-Infrared Astronomy Research Laboratory (NSF’s OIR Lab)

 

The DESI collaboration is honored to be permitted to conduct scientific research on oligam Du’ag, a mountain with particular significance to the Tohono O’odham Nation.

Credit: KPNO/NOIRLab/NSF/AURA/P. Horálek (Institute of Physics in Opava)

The DESI collaboration is honored to be permitted to conduct scientific research on oligam Du’ag, a mountain with particular significance to the Tohono O’odham Nation.

Credit: KPNO/NOIRLab/NSF/AURA/P. Horálek (Institute of Physics in Opava)

Credit: Claire Lamman

DESI timeline

2015

16

17

18

19

20

22

23

24

21

25

26

27

approved

construction started

first light

comissioning completed

main survey started

Y1 data sample

Y1 results

Y3 data sample secured

end of the survey...

... DESI-2

From images to redshifts

imaging surveys (2014 - 2019) + WISE (IR)

target selection

spectroscopic observations

spectra and redshift measurements

Imaging surveys used by DESI

\(\sim 22.7\) in SDSS

\(r\)-band depth

Bok

Imaging surveys used by DESI

\(\sim 23.7\) in North

\(\sim 22.7\) in SDSS

\(r\)-band depth

Bok

Mayall

credit: NOIRLab

Optical surveys (grz)

North (5.2k \(\mathrm{deg}^2\))
- BASS (gr): 2016 - 2018

- MzLS (z): 2015 - 2019

 

Imaging surveys used by DESI

\(\sim 23.7\) in North

\(\sim 22.7\) in SDSS

\(r\)-band depth

Optical surveys (grz)

North (5.2k \(\mathrm{deg}^2\))
- BASS (gr): 2016 - 2018

- MzLS (z): 2015 - 2019

South (11.7k \(\mathrm{deg}^2\))

- DECaLS (grz): 2014 - 2019

\(\sim 24.2\) in South

Blanco

Imaging surveys used by DESI

Optical surveys (grz)

North (5.2k \(\mathrm{deg}^2\))
- BASS (gr): 2016 - 2018

- MzLS (z): 2015 - 2019

South (11.7k \(\mathrm{deg}^2\))

- DECaLS (grz): 2014 - 2019

Dey et al. 2019

Imaging surveys used by DESI

Infrared survey

WISE & NEOWISE (W1, W2, W3, W4): 2010 - 2020

Blanco

Wright et al. 2010

Imaging surveys used by DESI

visit legacysurvey.org

Imaging surveys used by DESI

Target selection - BGS (\(0.1 < z < 0.4\))

BGS bright \(\simeq\) 850 targets \(\mathrm{deg}^{-2}\)

BGS faint \(\simeq\) 520 targets \(\mathrm{deg}^{-2}\)

DESI Collaboration, arXiv:2306.06307

\(19.5 < r < 20.2\)

\(r < 19.5\)

Target selection - LRG (\(0.4 < z < 1.1\))

a) star rejection

b) redshift

c) density

d) spectro S/N

\(\simeq\) 640 targets \(\mathrm{deg}^{-2}\)

a) star rejection with WISE

b) \(g - W1 > 2.9\) selects targets with \(z > 0.3\)

c) slope of \(r - W1\) vs \(W1\) chosen to produce ~ constant number density \(0.4 < z < 0.8\)

d) spectro S/N with z-fiber cut

Target selection - ELG (\(0.6 < z < 1.6\))

\(\simeq\)  1940 targets \(\mathrm{deg}^{-2}\)

a) number density tuned with \(g\mathrm{fiber} < 24.1\)

b) star / low-\(z\) rejection with \(g - r\) vs \(r - z\)

c) rejection of \(z > 1.6\) with \(g - r\) cut

d) high [OII] with \(g - r\) vs \(r - z\)

c) high-z

b) star / low-z rejection

d) [OII]

Target selection - QSO (\(0.8 < z < 3.5\))

a) PSF-type objects

b) 16.5 < r < 23 cut to remove bright stars, low S/N spectro

c) QSO separated from stars with excess infrared from the dusty torus: W1, W2 > 22.3 and random forest trained on grzW1W2 colors

stellar locus

\(\simeq\)  310 targets \(\mathrm{deg}^{-2}\)

Target selection

Target selection

Raichoor et al. 2022 arXiv:2208.08513

From images to redshifts

imaging surveys (2014 - 2019) + WISE (IR)

target selection

spectroscopic observations

spectra and redshift measurements

Mayall Telescope

focal plane 5000 fibers

wide-field corrector

6 lenses, FoV \(\sim 8~\mathrm{deg}^{2}\)

Kitt Peak, AZ

4 m mirror

Mayall Telescope

focal plane 5000 fibers

fiber view camera

ten 3-channel spectrographs

49 m, 10-cable fiber run

Kitt Peak, AZ

Mayall Telescope

Credit: Claire Lamman

For more BaoBan adventures, see cmlamman.github.io/baoban_comics.html

Focal plane: 5000 robotic positioners

86 cm

GFA: Guide/Focus/Alignment

Focal plane: 5000 robotic positioners

Focal plane: 5000 robotic positioners

Robotic positioner

= 1.4 arcsec (~seeing)

2 DoF \((\Theta, \Phi)\): 2 motors in open-loop

Fiber view camera

fibers illuminated from spectrographs

FVC takes image through the corrector

positioning: "blind" move (50\(\mu\mathrm{m}\)), "correction" move (6\(\mu\mathrm{m}\))

"Fiber collisions"

Pinon et al. 2024, arXiv:2406.04804

Groups of galaxies too close to each other cannot all receive a fiber

\(0.05^\circ \simeq\) positioner patrol diameter

Observing sequence

Reposition & readout in <2min!

Exposure time (dark) 1000 s

 

Spectrographs

fibers illuminated from spectrographs

FVC takes image through the corrector

positioning

10 identical 500 fiber spectrographs

3 arms (red, blue, NIR)

Linear Pulse Tube cooled

French technical contribution

(CEA, CNRS)

Vendor (French!)

Spectroscopic pipeline

wavelength

fiber number

\(z = 2.1\) QSO

\(z = 0.9\) ELG

Ly\(\alpha\)

CIV

CIII

[OII] doublet at \(2727 \AA\) up to \(z = 1.6\)

[OII]

Ly\(\alpha\) at \(1216 \AA\) down to \(z = 2.0\)

Survey strategy

Full Survey: 14,000 \(\mathrm{deg}^{2}\)

Field of view: 8 \(\mathrm{deg}^{2}\) \(\simeq\) 42 full moon

dark time: LRG, ELG, QSO - 7 passes       bright time: BGS - 4 passes

Schlafly et al. 2023 arXiv:2306.06309

Survey strategy

Full Survey: 14,000 \(\mathrm{deg}^{2}\)

asgn. comp. (Y5) z. comp. #good z (Y5)
BGS 80% 99% 13.8M
LRG 90% 99% 7.5M
ELG 60% 73% 15.7M
QSO 99% 67% 2.9M
\frac{\sigma_P}{P} \propto \frac{1}{V} \frac{P + 1/\bar{n}}{P}

DESI data release 1 (DR1)

Observations from May 14th 2021 to June 12th 2022

DESI data release 1 (DR1)

Observations from May 14th 2021 to June 12th 2022

asgn. comp. Y1 / Y5
BGS 64% 40%
LRG 69% 30%
ELG 35% 21%
QSO 87% 50%

The Contreras Fire (June 11 - 17 2022)

The Contreras Fire (June 11 - 17 2022)

Credit: Bob Stupak

The Contreras Fire (June 11 - 17 2022)

Credit: Clara Delabrouille

DESI data release 1 (DR1)

5.7 million unique redshifts at z < 2.1 and > 420,000 Ly\(\alpha\) QSO at z > 2.1

Release of DESI Y1 (BAO) results

April 4th 2024

First batch of DESI Y1 cosmological analyses
data.desi.lbl.gov/doc/papers/


• DESI 2024 I: First year data release
• DESI 2024 II: DR1 catalogs
• DESI 2024 III: BAO from Galaxies and Quasars
• DESI 2024 IV: BAO from the Lyman-Forest
• DESI 2024 V: RSD from Galaxies and Quasars
• DESI 2024 VI: Cosmological constraints from BAO measurements
• DESI 2024 VII: Cosmological constraints from RSD measurements

Release of DESI Y1 (BAO) results

April 4th 2024

First batch of DESI Y1 cosmological analyses
data.desi.lbl.gov/doc/papers/


• DESI 2024 I: First year data release
• DESI 2024 II: DR1 catalogs
• DESI 2024 III: BAO from Galaxies and Quasars
• DESI 2024 IV: BAO from the Lyman-Forest
• DESI 2024 V: RSD from Galaxies and Quasars
• DESI 2024 VI: Cosmological constraints from BAO measurements
• DESI 2024 VII: Cosmological constraints from RSD measurements

Correlation functions

Correlation functions

BAO peak

Power spectra

Power spectra

BAO wiggles

Some fits: configuration space

isotropic measurement

anisotropic measurement

\propto (D_{\mathrm{M}}^{2}(z) D_\mathrm{H}(z))^{1/3} / r_\mathrm{d}
\propto D_{\mathrm{M}}(z) / D_\mathrm{H}(z)

Some fits: Fourier space

isotropic measurement

anisotropic measurement

\propto (D_{\mathrm{M}}^{2}(z) D_\mathrm{H}(z))^{1/3} / r_\mathrm{d}
\propto D_{\mathrm{M}}(z) / D_\mathrm{H}(z)

Non-linear evolution

Non-linear structure growth and peculiar velocities blur and shrink (slightly) the ruler

Eisenstein et al. 2008, Padmanabhan et al. 2012

Density field reconstruction

Estimates Zeldovich displacements from observed field and moves galaxies back: refurbishes the ruler (improves precision and accuracy)

reconstruction

Density field reconstruction

DESI Y1 BAO analysis

  • Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))

5.7 million unique redshifts

Effective volume \(V_\mathrm{eff} = 18 \; \mathrm{Gpc}^{3}\)

\(3 \times \) bigger than SDSS!

DESI Y1 BAO analysis

  • Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))
  • Blind analysis to mitigate observer / confirmation biases (catalog-level blinding)
(\mathrm{R.A.}, \mathrm{Dec.}, z) \Longrightarrow (x, y, z) \Longrightarrow (\mathrm{R.A.}^\prime, \mathrm{Dec.}^\prime, z^\prime)

fiducial cosmology

blinded cosmology (\(\Omega_\mathrm{m}, w_0, w_a\))

(random & unknown)

DESI Y1 BAO analysis

  • Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))
  • Blind analysis to mitigate observer / confirmation biases (catalog-level blinding)
(\mathrm{R.A.}, \mathrm{Dec.}, z) \Longrightarrow (x, y, z) \Longrightarrow (\mathrm{R.A.}^\prime, \mathrm{Dec.}^\prime, z^\prime)

fiducial cosmology

blinded cosmology (\(\Omega_\mathrm{m}, w_0, w_a\))

(random & unknown)

+ RSD blinding: change reconstructed peculiar velocities

+ \(f_\mathrm{NL}\) blinding: add clustering-dependent signal on large scales with weights

DESI Y1 BAO analysis

  • Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))
  • Blind analysis to mitigate observer / confirmation biases (catalog-level blinding)
  • Theory developments in BAO fitting code
P_\mathrm{gg}(k, \mu) = \mathcal{B}(k, \mu) P_\mathrm{nw}(k) + \mathcal{C}(k, \mu) P_\mathrm{w}(k) + \orange{\mathcal{D}(k, \mu)}

Chen, Howlett et al. 2024, arXiv:2402.14070

DESI Y1 BAO analysis

  • Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))
  • Blind analysis to mitigate observer / confirmation biases (catalog-level blinding)
  • Theory developments in BAO fitting code
  • New and improved reconstruction methods
  • New combined tracer method used for overlapping galaxy samples (LRG and ELG in \(0.8 < z < 1.1\))

DESI Y1 BAO analysis

  • Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))
  • Blind analysis to mitigate observer / confirmation biases (catalog-level blinding)
  • Theory developments in BAO fitting code
  • New and improved reconstruction methods
  • New combined tracer method used for overlapping galaxy samples (LRG and ELG in \(0.8 < z < 1.1\))
  • Unified BAO pipeline applied to all (discrete) tracer / redshift bins consistently

Tests of systematic errors

Considered many possible sources of systematic errors using simulations and data:

  • observational effects (imaging systematics, fiber collisions)
  • BAO reconstruction (2 algorithms compared)
  • covariance matrix construction
  • incomplete theory modelling
  • choice of fiducial cosmology
  • galaxy-halo (HOD) model uncertainties

no systematics detected

systematics << statistics

Max effect: \(\sigma_\mathrm{stat. + syst.} < 1.05 \sigma_\mathrm{stat.}\)

Release of DESI Y1 (BAO) results

April 4th 2024

First batch of DESI Y1 cosmological analyses
https://data.desi.lbl.gov/doc/papers/


• DESI 2024 I: First year data release
• DESI 2024 II: DR1 catalogs
• DESI 2024 III: BAO from Galaxies and Quasars
• DESI 2024 IV: BAO from the Lyman-Forest
• DESI 2024 V: RSD from Galaxies and Quasars
• DESI 2024 VI: Cosmological constraints from BAO measurements
• DESI 2024 VII: Cosmological constraints from RSD measurements

Ly\(\alpha\) forest

Absorption in QSO spectra by neutral hydrogen in the intergalactic medium: \(\lambda_\mathrm{abs} = (1 + z_\mathrm{HI}) \times 1215.17 \; \AA \)

Transmitted flux fraction \(F = e^{-\tau}\) probes the fluctuation in neutral hydrogen density, \(\tau \propto n_\mathrm{HI} \)

credit: Andrew Pontzen

Ly\(\alpha\) correlation functions in DESI Y1

Ly\(\alpha\) - Ly\(\alpha\)

Ly\(\alpha\) - QSO

QSO

QSO

HI cloud

HI cloud

HI cloud

QSO

DESI Y1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))

>420,000 Ly\(\alpha\) QSO at z > 2.1

\(2 \times \) more than SDSS!

DESI Y1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
  • First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)

DESI Y1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
  • First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
  • Modelling of the correlation function: cosmological signal, and many contaminants!

DESI Y1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
  • First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
  • Modelling of the correlation function: cosmological signal, and many contaminants!
  • Very stable results, systematic uncertainty neglected

Release of DESI Y1 (BAO) results

April 4th 2024

First batch of DESI DR1 cosmological analyses
https://data.desi.lbl.gov/doc/papers/


• DESI 2024 I: First year data release
• DESI 2024 II: DR1 catalogs
• DESI 2024 III: BAO from Galaxies and Quasars
• DESI 2024 IV: BAO from the Lyman-Forest
• DESI 2024 V: RSD from Galaxies and Quasars
• DESI 2024 VI: Cosmological constraints from BAO measurements
• DESI 2024 VII: Cosmological constraints from RSD measurements

  • transverse to the line-of-sight: \(D_\mathrm{M}(z) / r_\mathrm{d}\)
  • along the line-of-sight: \(D_\mathrm{H}(z) / r_\mathrm{d} = c / (H(z) r_\mathrm{d}) \)
  • low S/N, isotropic average: \( D_\mathrm{V}(z) / r_\mathrm{d} = (z D_{\mathrm{M}}^{2}(z) D_\mathrm{H}(z))^{1/3} / r_\mathrm{d}\)

BAO measurements

BAO measures ratios of distances over the sound horizon scale at the drag epoch ["standard ruler"] \(r_\mathrm{d}\)

Let's factor out the \(h\) terms:

  • ​\(\color{blue}{[D_\mathrm{M}(z) h] (\Omega_\mathrm{m}, f_\mathrm{DE}, \Omega_\mathrm{K}, ...)} \color{black}{/} \color{orange}{[r_\mathrm{d}(\Omega_\mathrm{m} h^{2}, \Omega_\mathrm{b} h^{2}) h]} \)
  • \( \color{blue}{[D_\mathrm{H}(z) h] (\Omega_\mathrm{m}, f_\mathrm{DE}, \Omega_\mathrm{K}, ...)} \color{black}{/} \color{orange}{[r_\mathrm{d}(\Omega_\mathrm{m} h^{2}, \Omega_\mathrm{b} h^{2}) h]} \)

BAO measurements at different \(z\) constrain:

  • energy content \( \color{blue}{(\Omega_\mathrm{m}, f_\mathrm{DE}, ...)} \)
  • constant-over-\(z\) product \(\color{orange}{r_\mathrm{d} h}\) i.e. \(\color{orange}{H_{0} r_\mathrm{d}}\)

These quantities directly relate to base cosmological parameters

BAO measurements

\(h = H_{0} / [100\; \mathrm{km}/\mathrm{s} / \mathrm{Mpc}]\)

\(\Omega_\mathrm{m}\) fractional energy density of matter

\(f_\mathrm{DE}\) dark energy

\(\Omega_\mathrm{K}\) curvature

\(\Omega_{b}\) baryons

DESI Y1 BAO

DESI BAO measurements

DESI Y1 BAO

DESI BAO measurements

DESI Y1 BAO

DESI BAO measurements

DESI Y1 BAO

DESI BAO measurements

DESI Y1 BAO

DESI BAO measurements

DESI Y1 BAO

DESI BAO measurements

DESI Y1 BAO

DESI BAO measurements

Consistent with each other,

and complementary

\underbrace{\begin{align*} \Omega_\mathrm{m} &= 0.295 \pm 0.015 & \mathbf{(5.1\%)} \\ H_{0} r_\mathrm{d} &= (101.8 \pm 1.3) \, [100 \, \mathrm{km} \, \mathrm{s}^{-1}] & \mathbf{(1.3\%)} \end{align*}}_{\textstyle \text{\color{black}{DESI}}}

Consistency with other probes

DESI Y1 BAO consistent with:

Consistency with other probes

DESI Y1 BAO consistent with:

Consistency with other probes

DESI Y1 BAO consistent with:

Consistency with other probes

DESI Y1 BAO consistent with:

\underbrace{ \Omega_\mathrm{m} = 0.3069 \pm 0.0050 \; \mathbf{(1.6\%)} }_{\textstyle \text{\green{DESI + CMB}}}
  • BAO constrains \( r_\mathrm{d}(\blue{\Omega_\mathrm{m}} h^{2}, \orange{\Omega_\mathrm{b} h^{2}}) h \)
  • \( \blue{\Omega_\mathrm{m}} \) constrained by BAO at different \(z\)
  • \(\orange{\Omega_\mathrm{b}h^2}\) can be constrained by light element abundance from Big Bang Nucleosynthesis (BBN): Schöneberg et al., 2024

\(\implies\) constraints on \(h\) i.e. \(H_0 = 100 h \; \mathrm{km} / \mathrm{s} / \mathrm{Mpc}\)

Hubble constant

\underbrace{ H_0 = (68.53 \pm 0.80) \, {\rm km\,s^{-1}\,Mpc^{-1}} }_{\textstyle \color{darkblue}{\text{DESI} + \text{BBN}}}

Hubble constant

\underbrace{ H_0 = (68.52 \pm 0.62) \, {\rm km\,s^{-1}\,Mpc^{-1}} }_{\textstyle \color{darkblue}{\text{DESI} + \theta_\ast + \text{BBN}}}

\(\theta_\ast\) CMB angular acoustic scale

 

​

 

  • Consistency with SDSS
\underbrace{ H_0 = (68.53 \pm 0.80) \, {\rm km\,s^{-1}\,Mpc^{-1}} }_{\textstyle \color{darkblue}{\text{DESI} + \text{BBN}}}

Hubble constant

\underbrace{ H_0 = (68.52 \pm 0.62) \, {\rm km\,s^{-1}\,Mpc^{-1}} }_{\textstyle \color{darkblue}{\text{DESI} + \theta_\ast + \text{BBN}}}

 

​

 

  • Consistency with SDSS
  • In agreement with CMB
\underbrace{ H_0 = (68.53 \pm 0.80) \, {\rm km\,s^{-1}\,Mpc^{-1}} }_{\textstyle \color{darkblue}{\text{DESI} + \text{BBN}}}

Hubble constant

\underbrace{ H_0 = (68.52 \pm 0.62) \, {\rm km\,s^{-1}\,Mpc^{-1}} }_{\textstyle \color{darkblue}{\text{DESI} + \theta_\ast + \text{BBN}}}


​


  • Consistency with SDSS
  • In agreement with CMB
  • In \(3.7 \sigma\) tension with SH0ES
\underbrace{ H_0 = (68.53 \pm 0.80) \, {\rm km\,s^{-1}\,Mpc^{-1}} }_{\textstyle \color{darkblue}{\text{DESI} + \text{BBN}}}

Hubble constant

\underbrace{ H_0 = (68.52 \pm 0.62) \, {\rm km\,s^{-1}\,Mpc^{-1}} }_{\textstyle \color{darkblue}{\text{DESI} + \theta_\ast + \text{BBN}}}

DESI + CMB measurements favor a flat Universe

\Omega_\mathrm{K} = 0.0024 \pm 0.0016 \; \green{(\text{DESI} + \text{CMB})}

Spatial curvature

Dark Energy Equation of State

Dark Energy fluid, pressure \(p\), density \(\rho\)

Equation of State parameter \(w = p / \rho\)

Linked to the evolution of Dark Energy \(w(z) = -1 + \frac{1}{3}\frac{d \ln f_\mathrm{DE}(z)}{d \ln (1 + z)}\)

Dark Energy Equation of State

\underbrace{\begin{align*} \Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\ w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)} \end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}

Constant EoS parameter \(w = p / \rho\)

\Lambda

Dark Energy Equation of State

\underbrace{\begin{align*} \Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\ w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)} \end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}

Constant EoS parameter \(w = p / \rho\)

Dark Energy Equation of State

SNe:

\underbrace{\begin{align*} \Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\ w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)} \end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}

Constant EoS parameter \(w = p / \rho\)

Dark Energy Equation of State

SNe:

\underbrace{\begin{align*} \Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\ w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)} \end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}

Constant EoS parameter \(w = p / \rho\)

Dark Energy Equation of State

SNe:

\underbrace{\begin{align*} \Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\ w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)} \end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}

Constant EoS parameter \(w = p / \rho\)

Dark Energy Equation of State

\underbrace{\begin{align*} \Omega_\mathrm{m} &= 0.3095 \pm 0.0065 & \mathbf{(2.1\%)} \\ w &= -0.997 \pm 0.025 & \mathbf{(2.5\%)} \end{align*}}_{\textstyle \text{\color{orange}{DESI + CMB + Pantheon+}}}

Assuming a constant EoS, DESI BAO fully compatible with a cosmological constant...

\underbrace{\begin{align*} \Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\ w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)} \end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}

Constant EoS parameter \(w = p / \rho\)

w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}
\left. \begin{align*} w_{0} &= -0.55^{+0.39}_{-0.21} \\ w_{a} &< -1.32 \end{align*} \right\rbrace {\text{\color{black}{DESI}}}

Dark Energy Equation of State

Varying EoS

\Lambda

Dark Energy Equation of State

\underbrace{ w_{0} = -0.45^{+0.34}_{-0.21} \qquad w_{a} = -1.79^{+0.48}_{-1.00} }_{\textstyle \purple{\text{DESI + CMB} \; \implies \; 2.6\sigma}}

Varying EoS

w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}

Dark Energy Equation of State

\underbrace{ w_{0} = -0.45^{+0.34}_{-0.21} \qquad w_{a} = -1.79^{+0.48}_{-1.00} }_{\textstyle \purple{\text{DESI + CMB} \; \implies \; 2.6\sigma}}

Varying EoS

w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}

Dark Energy Equation of State

\underbrace{ w_{0} = -0.45^{+0.34}_{-0.21} \qquad w_{a} = -1.79^{+0.48}_{-1.00} }_{\textstyle \purple{\text{DESI + CMB} \; \implies \; 2.6\sigma}}

Varying EoS

w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}

Dark Energy Equation of State

\underbrace{ w_{0} = -0.45^{+0.34}_{-0.21} \qquad w_{a} = -1.79^{+0.48}_{-1.00} }_{\textstyle \purple{\text{DESI + CMB} \; \implies \; 2.6\sigma}}

Varying EoS

w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}

Dark Energy Equation of State

Combining all DESI + CMB + SN

\underbrace{ w_{0} = -0.827 \pm 0.063 \qquad w_{a} = -0.75^{+0.29}_{-0.25} }_{\textstyle \blue{\text{DESI + CMB + Pantheon+} \; \implies \; 2.5\sigma}}

Dark Energy Equation of State

Combining all DESI + CMB + SN

\underbrace{ w_{0} = -0.827 \pm 0.063 \qquad w_{a} = -0.75^{+0.29}_{-0.25} }_{\textstyle \blue{\text{DESI + CMB + Pantheon+} \; \implies \; 2.5\sigma}}
\underbrace{ w_{0} = -0.64 \pm 0.11\qquad w_{a} = -1.27^{+0.40}_{-0.34} }_{\textstyle \color{orange}{\text{DESI + CMB + Union3} \; \implies \; 3.5\sigma}}

Dark Energy Equation of State

Combining all DESI + CMB + SN

\underbrace{ w_{0} = -0.827 \pm 0.063 \qquad w_{a} = -0.75^{+0.29}_{-0.25} }_{\textstyle \blue{\text{DESI + CMB + Pantheon+} \; \implies \; 2.5\sigma}}
\underbrace{ w_{0} = -0.64 \pm 0.11\qquad w_{a} = -1.27^{+0.40}_{-0.34} }_{\textstyle \color{orange}{\text{DESI + CMB + Union3} \; \implies \; 3.5\sigma}}
\underbrace{ w_{0} = -0.727 \pm 0.067 \qquad w_{a} = -1.05^{+0.31}_{-0.27} }_{\textstyle \color{green}{\text{DESI + CMB + DES-SN5YR} \; \implies \; 3.9\sigma}}

Dark Energy Equation of State

Combining all DESI + CMB + SN

\(w_{0} > -1, w_{a} < 0\) favored, level varying on the SN dataset

\underbrace{ w_{0} = -0.827 \pm 0.063 \qquad w_{a} = -0.75^{+0.29}_{-0.25} }_{\textstyle \blue{\text{DESI + CMB + Pantheon+} \; \implies \; 2.5\sigma}}
\underbrace{ w_{0} = -0.64 \pm 0.11\qquad w_{a} = -1.27^{+0.40}_{-0.34} }_{\textstyle \color{orange}{\text{DESI + CMB + Union3} \; \implies \; 3.5\sigma}}
\underbrace{ w_{0} = -0.727 \pm 0.067 \qquad w_{a} = -1.05^{+0.31}_{-0.27} }_{\textstyle \color{green}{\text{DESI + CMB + DES-SN5YR} \; \implies \; 3.9\sigma}}

Sum of neutrino masses

Internal CMB degeneracies limiting precision on the sum of neutrino masses

Sum of neutrino masses

Internal CMB degeneracies limiting precision on the sum of neutrino masses

Broken by BAO, especially through \(H_{0}\)

Low preferred value of \(H_{0}\) yields

\(\sum m_\nu < 0.072 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}\)

Limit relaxed for extensions to \(\Lambda\mathrm{CDM}\)

\(\sum m_\nu < 0.195 \, \mathrm{eV}\) for \(w_0w_a\mathrm{CDM}\)

Neutrino mass hierarchies

\sum m_\nu < 0.113 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}

With \(> 0.059 \, \mathrm{eV}\) prior (NH)

Neutrino mass hierarchies

\sum m_\nu < 0.113 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}
\sum m_\nu < 0.145 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}

With \(> 0.059 \, \mathrm{eV}\) prior (NH)

With \(> 0.1 \, \mathrm{eV}\) prior (IH)

Neutrino mass hierarchies

\sum m_\nu < 0.113 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}
\sum m_\nu < 0.145 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}

With \(> 0.059 \, \mathrm{eV}\) prior (NH)

With \(> 0.1 \, \mathrm{eV}\) prior (IH)

Current constraints do not strongly favor normal over inverted hierarchy (\(\simeq 2 \sigma\))

Y1 BAO constraints: a summary

DESI already has the most precise BAO measurements ever

Y1 BAO constraints: a summary

DESI already has the most precise BAO measurements ever

 

DESI BAO is consistent (at the \(\sim 1.9\sigma\) level) with CMB in flat ΛCDM

Y1 BAO constraints: a summary

DESI already has the most precise BAO measurements ever

 

DESI BAO is consistent (at the \(\sim 1.9\sigma\) level) with CMB in flat ΛCDM

 

In flat ΛCDM, DESI prefers "small \(\Omega_\mathrm{m}\), large \(H_0\) (though \(3.7\sigma\) away from SH0ES), small \(\sum m_\nu\)"

Y1 BAO constraints: a summary

DESI already has the most precise BAO measurements ever

 

DESI BAO is consistent (at the \(\sim 1.9\sigma\) level) with CMB in flat ΛCDM

 

In flat ΛCDM, DESI prefers "small \(\Omega_\mathrm{m}\), large \(H_0\) (though \(3.7\sigma\) away from SH0ES), small \(\sum m_\nu\)"

 

Some hint of time-varying Dark Energy equation of state especially when combined with supernovae measurements

What I haven't talked about

Y1 supporting papers: BAO and Full Shape theory modelling, covariance matrices, BAO reconstruction, etc., see data.desi.lbl.gov/doc/papers/

 

 

DESI EDR data public (including 1%: 140 \(\mathrm{deg}^2\), 1.2M extragalactic redshifts): DESI Collaboration 2023 arXiv:2306.06308

A bunch of science papers: Ly\(\alpha\), small scale clustering (HOD), etc., see: data.desi.lbl.gov/doc/papers/edr/

Conclusions

DESI runs beautifully!

 

Y1 full shape analysis unblinded, papers at the end of 2024

 

Many alternative analyses! DE reconstruction, \(H_0\) without BAO, modified gravity, higher order statistics, alternative statistics, etc.


DR1 catalogs to be available next year

 

Y3 data on disk, BAO analysis starting!

Full shape analysis

observed redshift = Hubble flow and peculiar velocities (RSD = "redshift space distortions")

\propto f\sigma_8

Full shape also driven by primordial physics (\(\omega_m, \omega_b, n_s, f_{\mathrm{NL}}, ...\))

RSD probes growth of structure \(f\sigma_8\), sensitive to gravity, DE, \(\nu\)

Full shape analysis

Three power spectrum EFT models considered:

- pybird

- velocileptors

- folps

V = 200 \; [\mathrm{Gpc}/h]^3

credit: Mark Maus, Hernan Noriega, Yan Lai

Maus et al. 2024

Lai et al. 2024

Noriega et al. 2024

Maus et al. 2024

Full shape analysis - tests

- maximum fitting scale \(k_\mathrm{max}\)

- galaxy - halo connection, bias parametrization, prior choices

- "ShapeFit" template compression, Brieden 2021

- fiducial cosmology

- covariance matrix

- prior volume effects

Full shape analysis

Prior volume effects

credit: Ruiyang Zhao

Full shape analysis

- \(k_\mathrm{max}\)

- galaxy - halo connection, bias parametrization, prior choices

- "ShapeFit" template compression, Brieden 2021

- fiducial cosmology

- covariance matrix

- prior volume effects

- imaging systematics

- spectroscopic systematics

- "fiber collisions"

Fiber collisions

Impacts power spectrum measurements (altMTL vs complete)

 

Fiber collisions

Impacts power spectrum measurements (altMTL vs complete)

Solution: \(\theta\)-cut = remove all pairs \(< 0.05^\circ\), new window matrix

Fiber collisions

New window matrix \(W^\mathrm{cut}\); \(\langle P_o(k) \rangle = W^\mathrm{cut}(k, k^\prime) P_t(k^\prime)\)

 

Fiber collisions

New window matrix \(W^\mathrm{cut}\); \(\langle P_o(k) \rangle = W^\mathrm{cut}(k, k^\prime) P_t(k^\prime)\)

Very non diagonal: let's "rotate" it

 

Fiber collisions

Successfully removes the \( > 1 \sigma\) bias

 

credit: Ruiyang Zhao

Full shape analysis

Tests: stability with \(k_\mathrm{max}\)

V = 8 \; [\mathrm{Gpc}/h]^3

credit: Mark Maus

Full shape analysis

Tests: bias parameterization

  • maximal freedom: all 4 bias parameter free
  • minimal freedom: \(b_s, b_{3}\) fixed (co-evolution)
V = 200 \; [\mathrm{Gpc}/h]^3

credit: Hernan Noriega

DESI DR1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
  • First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
  • Modelling of the correlation function:
    • cosmo signal
P_{\mathrm{Ly}\alpha}(k, \mu) = \blue{b^{2} (1 + \beta \mu^2)^2} \orange{P_\mathrm{lin}(k, \mu)} \green{F_\mathrm{NL}(k, \mu)}

linear bias + RSD

hydro-sim

BAO

\mu = r_\parallel / \sqrt{r_\parallel^2 + r_\perp^2}

DESI DR1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
  • First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
  • Modelling of the correlation function:
    • cosmo signal
    • high-column density
    • metal absorbers
r_\parallel \propto \left\vert\frac{1}{\lambda_{\mathrm{Ly}\alpha}} - \frac{1}{\lambda_{\mathrm{metal}}}\right\vert

SiII

DESI DR1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
  • First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
  • Modelling of the correlation function:
    • cosmo signal
    • high-column density
    • metal absorbers
    • correlated noise (sky subtraction)

DESI DR1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
  • First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
  • Modelling of the correlation function

physical model fit

+ broadband polynomial

 

broadband: \(< 0.1\sigma\)

DESI DR1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
  • First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
  • Modelling of the correlation function
  • Cross-covariance matrix

Correlation matrix

smoothed jackknife, validated with mocks

10% impact on BAO uncertainty

DESI DR1 Ly\(\alpha\) BAO analysis

  • Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
  • First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
  • Modelling of the correlation function
  • Cross-covariance matrix
  • Very stable results, systematic uncertainty neglected

Tests of systematic errors

tests with same dataset (not red): shifts \(< \sigma_\mathrm{stat}/3\)

tests with varying datasets (red): shifts consistent with stat.

desilike

- BAO, full shape likelihoods, designed to extend to other observables (lensing, etc.)

- wraps PT codes: velocileptors, pybird, folps(ax)

- automated cobaya / cosmosis / montepython bindings

- wraps samplers, profilers, fisher, in-place emulation

- "JAXification"

template = DirectPowerSpectrumTemplate(z=1.)
theory = LPTVelocileptorsTracerPowerSpectrumMultipoles(ells=(0, 2, 4), template=template)
theory(h=0.7, b1p=1.2)  # returns pk
observable = TracerPowerSpectrumMultipoles(data=data, wmatrix=wmatrix, theory=theory,
										   klim={0: (0.02, 0.2), 2: (0.02, 0.2)})
likelihood = ObservablesGaussianLikelihood(observables=observable)
likelihood(Omega_m=0.3)  # returns log-posterior

Other datasets

\(\sum m_\nu\)

credit: Christophe Yèche

\(\sum m_\nu\)

credit: Christophe Yèche

\(w(z)\)

DESI - SDSS consistency (\(\Omega_\mathrm{m}\))

Perfectly consistent!

Using these 2 points alone moves \(\Omega_\mathrm{m}\) by \(< 2 \sigma\)

Are SN \(\Omega_\mathrm{m}\) consistent?

Not so much in flat \(\Lambda\mathrm{CDM}\)...

(so we do not combine them in this model!)

Are SN \(\Omega_\mathrm{m}\) consistent?

Consistent in \(w_0w_a\mathrm{CDM}\)!

plik (PR3) vs PR4 Planck likelihoods

Appendix B

\(w_0 - w_a\) with \(\sum m_\nu\) free

\(w_0 - w_a\) with \(\Omega_\mathrm{K}\)

Preference for \(w_{0} > -1, w_{a} < 0\) persists when curvature is left free

DE constraints driven by low-\(z\) ?

Not that much!

 

DESI + SDSS swaps DESI measurements with SDSS for \(z < 0.6\)

 

\(- 0.4 \sigma\) compared to DESI only

\(w(z)\)

Dark energy equation of state:

\(P = w \rho\)

  • \(w\) = constant

BAO measurements: dark energy

Dark energy equation of state:

\(P = w \rho\)

  • CPL parameterization: \(w(a) = w_0 + (1 - a) w_a\)

BAO measurements: dark energy

Full tables

Full tables

Full tables

Full tables